Overview

Dataset statistics

Number of variables18
Number of observations893
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory125.7 KiB
Average record size in memory144.1 B

Variable types

Numeric7
Categorical8
Text3

Alerts

CPU is highly overall correlated with RamHigh correlation
Ram is highly overall correlated with CPUHigh correlation
Unnamed: 0 is highly overall correlated with Unnamed: 0.1High correlation
Unnamed: 0.1 is highly overall correlated with Unnamed: 0High correlation
price is highly overall correlated with resolution_height and 1 other fieldsHigh correlation
resolution_height is highly overall correlated with price and 1 other fieldsHigh correlation
resolution_width is highly overall correlated with price and 1 other fieldsHigh correlation
ROM is highly imbalanced (55.5%)Imbalance
ROM_type is highly imbalanced (83.9%)Imbalance
OS is highly imbalanced (75.7%)Imbalance
warranty is highly imbalanced (75.6%)Imbalance
Unnamed: 0.1 is uniformly distributedUniform
Unnamed: 0.1 has unique valuesUnique
Unnamed: 0 has unique valuesUnique

Reproduction

Analysis started2026-01-17 03:57:03.008912
Analysis finished2026-01-17 03:57:09.988770
Duration6.98 seconds
Software versionydata-profiling vv4.18.1
Download configurationconfig.json

Variables

Unnamed: 0.1
Real number (ℝ)

High correlation  Uniform  Unique 

Distinct893
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean467.1355
Minimum0
Maximum930
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2026-01-17T03:57:10.110512image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile45.6
Q1235
median467
Q3702
95-th percentile885.4
Maximum930
Range930
Interquartile range (IQR)467

Descriptive statistics

Standard deviation270.20977
Coefficient of variation (CV)0.57843981
Kurtosis-1.1999823
Mean467.1355
Median Absolute Deviation (MAD)234
Skewness-0.0090534777
Sum417152
Variance73013.319
MonotonicityStrictly increasing
2026-01-17T03:57:10.280444image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01
 
0.1%
6271
 
0.1%
6161
 
0.1%
6171
 
0.1%
6181
 
0.1%
6191
 
0.1%
6201
 
0.1%
6211
 
0.1%
6221
 
0.1%
6231
 
0.1%
Other values (883)883
98.9%
ValueCountFrequency (%)
01
0.1%
11
0.1%
21
0.1%
31
0.1%
41
0.1%
51
0.1%
61
0.1%
71
0.1%
81
0.1%
91
0.1%
ValueCountFrequency (%)
9301
0.1%
9291
0.1%
9281
0.1%
9271
0.1%
9261
0.1%
9251
0.1%
9241
0.1%
9231
0.1%
9221
0.1%
9211
0.1%

Unnamed: 0
Real number (ℝ)

High correlation  Unique 

Distinct893
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean521.38298
Minimum0
Maximum1019
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2026-01-17T03:57:10.486546image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile47.6
Q1265
median531
Q3784
95-th percentile973.4
Maximum1019
Range1019
Interquartile range (IQR)519

Descriptive statistics

Standard deviation299.9166
Coefficient of variation (CV)0.57523283
Kurtosis-1.2250711
Mean521.38298
Median Absolute Deviation (MAD)260
Skewness-0.064904162
Sum465595
Variance89949.97
MonotonicityStrictly increasing
2026-01-17T03:57:10.639655image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01
 
0.1%
7081
 
0.1%
6971
 
0.1%
6981
 
0.1%
6991
 
0.1%
7001
 
0.1%
7011
 
0.1%
7021
 
0.1%
7031
 
0.1%
7041
 
0.1%
Other values (883)883
98.9%
ValueCountFrequency (%)
01
0.1%
11
0.1%
21
0.1%
31
0.1%
41
0.1%
51
0.1%
61
0.1%
71
0.1%
81
0.1%
91
0.1%
ValueCountFrequency (%)
10191
0.1%
10181
0.1%
10171
0.1%
10161
0.1%
10151
0.1%
10141
0.1%
10131
0.1%
10121
0.1%
10111
0.1%
10101
0.1%

brand
Categorical

Distinct30
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Memory size7.1 KiB
HP
186 
Lenovo
169 
Asus
157 
Dell
107 
Acer
84 
Other values (25)
190 

Length

Max length9
Median length8
Mean length4.1881299
Min length2

Characters and Unicode

Total characters3740
Distinct characters44
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)0.8%

Sample

1st rowHP
2nd rowHP
3rd rowAcer
4th rowLenovo
5th rowApple

Common Values

ValueCountFrequency (%)
HP186
20.8%
Lenovo169
18.9%
Asus157
17.6%
Dell107
12.0%
Acer84
9.4%
MSI65
 
7.3%
Samsung28
 
3.1%
Apple16
 
1.8%
Infinix15
 
1.7%
LG9
 
1.0%
Other values (20)57
 
6.4%

Length

2026-01-17T03:57:10.778619image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
hp186
20.8%
lenovo169
18.9%
asus157
17.6%
dell107
12.0%
acer84
9.4%
msi65
 
7.3%
samsung28
 
3.1%
apple16
 
1.8%
infinix15
 
1.7%
lg9
 
1.0%
Other values (20)57
 
6.4%

Most occurring characters

ValueCountFrequency (%)
e403
 
10.8%
o364
 
9.7%
s361
 
9.7%
A260
 
7.0%
n240
 
6.4%
l240
 
6.4%
u206
 
5.5%
H190
 
5.1%
P187
 
5.0%
L180
 
4.8%
Other values (34)1109
29.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)3740
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e403
 
10.8%
o364
 
9.7%
s361
 
9.7%
A260
 
7.0%
n240
 
6.4%
l240
 
6.4%
u206
 
5.5%
H190
 
5.1%
P187
 
5.0%
L180
 
4.8%
Other values (34)1109
29.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)3740
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e403
 
10.8%
o364
 
9.7%
s361
 
9.7%
A260
 
7.0%
n240
 
6.4%
l240
 
6.4%
u206
 
5.5%
H190
 
5.1%
P187
 
5.0%
L180
 
4.8%
Other values (34)1109
29.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)3740
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e403
 
10.8%
o364
 
9.7%
s361
 
9.7%
A260
 
7.0%
n240
 
6.4%
l240
 
6.4%
u206
 
5.5%
H190
 
5.1%
P187
 
5.0%
L180
 
4.8%
Other values (34)1109
29.7%

name
Text

Distinct815
Distinct (%)91.3%
Missing0
Missing (%)0.0%
Memory size7.1 KiB
2026-01-17T03:57:11.236657image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/

Length

Max length75
Median length44
Mean length31.534155
Min length13

Characters and Unicode

Total characters28160
Distinct characters66
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique744 ?
Unique (%)83.3%

Sample

1st rowVictus 15-fb0157AX Gaming Laptop
2nd row15s-fq5007TU Laptop
3rd rowOne 14 Z8-415 Laptop
4th rowYoga Slim 6 14IAP8 82WU0095IN Laptop
5th rowMacBook Air 2020 MGND3HN Laptop
ValueCountFrequency (%)
laptop882
 
21.8%
gaming254
 
6.3%
2023124
 
3.1%
15106
 
2.6%
vivobook105
 
2.6%
ideapad77
 
1.9%
376
 
1.9%
inspiron59
 
1.5%
pro57
 
1.4%
1455
 
1.4%
Other values (975)2248
55.6%
2026-01-17T03:57:11.747919image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3162
 
11.2%
p1900
 
6.7%
o1846
 
6.6%
a1561
 
5.5%
11246
 
4.4%
01236
 
4.4%
t1079
 
3.8%
L1034
 
3.7%
5967
 
3.4%
2873
 
3.1%
Other values (56)13256
47.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)28160
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3162
 
11.2%
p1900
 
6.7%
o1846
 
6.6%
a1561
 
5.5%
11246
 
4.4%
01236
 
4.4%
t1079
 
3.8%
L1034
 
3.7%
5967
 
3.4%
2873
 
3.1%
Other values (56)13256
47.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)28160
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3162
 
11.2%
p1900
 
6.7%
o1846
 
6.6%
a1561
 
5.5%
11246
 
4.4%
01236
 
4.4%
t1079
 
3.8%
L1034
 
3.7%
5967
 
3.4%
2873
 
3.1%
Other values (56)13256
47.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)28160
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3162
 
11.2%
p1900
 
6.7%
o1846
 
6.6%
a1561
 
5.5%
11246
 
4.4%
01236
 
4.4%
t1079
 
3.8%
L1034
 
3.7%
5967
 
3.4%
2873
 
3.1%
Other values (56)13256
47.1%

price
Real number (ℝ)

High correlation 

Distinct464
Distinct (%)52.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean79907.41
Minimum9999
Maximum450039
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2026-01-17T03:57:11.875408image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/

Quantile statistics

Minimum9999
5-th percentile26995.4
Q144500
median61990
Q390990
95-th percentile199990
Maximum450039
Range440040
Interquartile range (IQR)46490

Descriptive statistics

Standard deviation60880.044
Coefficient of variation (CV)0.76188233
Kurtosis9.5323276
Mean79907.41
Median Absolute Deviation (MAD)22390
Skewness2.7096943
Sum71357317
Variance3.7063797 × 109
MonotonicityNot monotonic
2026-01-17T03:57:12.019202image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4999016
 
1.8%
3799013
 
1.5%
5999012
 
1.3%
4799011
 
1.2%
6499011
 
1.2%
5499011
 
1.2%
7999011
 
1.2%
3399011
 
1.2%
5799011
 
1.2%
6299011
 
1.2%
Other values (454)775
86.8%
ValueCountFrequency (%)
99991
 
0.1%
109903
0.3%
129901
 
0.1%
139901
 
0.1%
144901
 
0.1%
149901
 
0.1%
159902
0.2%
169901
 
0.1%
179901
 
0.1%
189902
0.2%
ValueCountFrequency (%)
4500391
0.1%
4299901
0.1%
4200001
0.1%
4199901
0.1%
4150001
0.1%
3999991
0.1%
3909141
0.1%
3629991
0.1%
3449901
0.1%
3399901
0.1%

spec_rating
Real number (ℝ)

Distinct30
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean69.379026
Minimum60
Maximum89
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2026-01-17T03:57:12.172339image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/

Quantile statistics

Minimum60
5-th percentile60
Q166
median69.323529
Q371
95-th percentile80
Maximum89
Range29
Interquartile range (IQR)5

Descriptive statistics

Standard deviation5.5415547
Coefficient of variation (CV)0.07987363
Kurtosis1.3926635
Mean69.379026
Median Absolute Deviation (MAD)2.3235294
Skewness0.8621133
Sum61955.471
Variance30.708828
MonotonicityNot monotonic
2026-01-17T03:57:12.294906image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
69.32352941292
32.7%
6048
 
5.4%
7144
 
4.9%
7043
 
4.8%
6242
 
4.7%
6439
 
4.4%
6739
 
4.4%
6637
 
4.1%
6537
 
4.1%
6935
 
3.9%
Other values (20)237
26.5%
ValueCountFrequency (%)
6048
5.4%
617
 
0.8%
6242
4.7%
6332
3.6%
6439
4.4%
6537
4.1%
6637
4.1%
6739
4.4%
686
 
0.7%
6935
3.9%
ValueCountFrequency (%)
895
0.6%
884
 
0.4%
864
 
0.4%
856
0.7%
844
 
0.4%
839
1.0%
826
0.7%
813
 
0.3%
8012
1.3%
7910
1.1%
Distinct184
Distinct (%)20.6%
Missing0
Missing (%)0.0%
Memory size7.1 KiB
2026-01-17T03:57:12.502930image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/

Length

Max length33
Median length31
Mean length27.18589
Min length5

Characters and Unicode

Total characters24277
Distinct characters51
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique84 ?
Unique (%)9.4%

Sample

1st row5th Gen AMD Ryzen 5 5600H
2nd row12th Gen Intel Core i3 1215U
3rd row11th Gen Intel Core i3 1115G4
4th row12th Gen Intel Core i5 1240P
5th rowApple M1
ValueCountFrequency (%)
gen838
16.1%
intel613
 
11.8%
core583
 
11.2%
i5287
 
5.5%
amd265
 
5.1%
ryzen257
 
4.9%
12th214
 
4.1%
13th207
 
4.0%
i7148
 
2.8%
11th132
 
2.5%
Other values (135)1667
32.0%
2026-01-17T03:57:13.015521image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4463
18.4%
e2376
 
9.8%
n1749
 
7.2%
11516
 
6.2%
t1449
 
6.0%
51193
 
4.9%
G933
 
3.8%
0887
 
3.7%
h832
 
3.4%
3819
 
3.4%
Other values (41)8060
33.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)24277
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4463
18.4%
e2376
 
9.8%
n1749
 
7.2%
11516
 
6.2%
t1449
 
6.0%
51193
 
4.9%
G933
 
3.8%
0887
 
3.7%
h832
 
3.4%
3819
 
3.4%
Other values (41)8060
33.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)24277
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4463
18.4%
e2376
 
9.8%
n1749
 
7.2%
11516
 
6.2%
t1449
 
6.0%
51193
 
4.9%
G933
 
3.8%
0887
 
3.7%
h832
 
3.4%
3819
 
3.4%
Other values (41)8060
33.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)24277
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4463
18.4%
e2376
 
9.8%
n1749
 
7.2%
11516
 
6.2%
t1449
 
6.0%
51193
 
4.9%
G933
 
3.8%
0887
 
3.7%
h832
 
3.4%
3819
 
3.4%
Other values (41)8060
33.2%

CPU
Categorical

High correlation 

Distinct29
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Memory size7.1 KiB
Quad Core, 8 Threads
130 
Hexa Core, 12 Threads
126 
10 Cores (2P + 8E), 12 Threads
125 
Octa Core, 16 Threads
102 
12 Cores (4P + 8E), 16 Threads
83 
Other values (24)
327 

Length

Max length31
Median length30
Mean length24.673012
Min length8

Characters and Unicode

Total characters22033
Distinct characters33
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)0.6%

Sample

1st rowHexa Core, 12 Threads
2nd rowHexa Core (2P + 4E), 8 Threads
3rd rowDual Core, 4 Threads
4th row12 Cores (4P + 8E), 16 Threads
5th rowOcta Core (4P + 4E)

Common Values

ValueCountFrequency (%)
Quad Core, 8 Threads130
14.6%
Hexa Core, 12 Threads126
14.1%
10 Cores (2P + 8E), 12 Threads125
14.0%
Octa Core, 16 Threads102
11.4%
12 Cores (4P + 8E), 16 Threads83
9.3%
Dual Core, 4 Threads55
6.2%
14 Cores (6P + 8E), 20 Threads50
 
5.6%
Hexa Core (2P + 4E), 8 Threads44
 
4.9%
Octa Core (4P + 4E), 12 Threads43
 
4.8%
Dual Core, 2 Threads36
 
4.0%
Other values (19)99
11.1%

Length

2026-01-17T03:57:13.113161image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
threads867
18.1%
core555
11.6%
421
 
8.8%
12382
 
8.0%
cores337
 
7.0%
8e277
 
5.8%
16222
 
4.6%
8178
 
3.7%
hexa170
 
3.6%
2p169
 
3.5%
Other values (19)1203
25.2%

Most occurring characters

ValueCountFrequency (%)
3888
17.6%
e1929
 
8.8%
r1759
 
8.0%
a1422
 
6.5%
s1204
 
5.5%
d1003
 
4.6%
C892
 
4.0%
o892
 
4.0%
T867
 
3.9%
h867
 
3.9%
Other values (23)7310
33.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)22033
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3888
17.6%
e1929
 
8.8%
r1759
 
8.0%
a1422
 
6.5%
s1204
 
5.5%
d1003
 
4.6%
C892
 
4.0%
o892
 
4.0%
T867
 
3.9%
h867
 
3.9%
Other values (23)7310
33.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)22033
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3888
17.6%
e1929
 
8.8%
r1759
 
8.0%
a1422
 
6.5%
s1204
 
5.5%
d1003
 
4.6%
C892
 
4.0%
o892
 
4.0%
T867
 
3.9%
h867
 
3.9%
Other values (23)7310
33.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)22033
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3888
17.6%
e1929
 
8.8%
r1759
 
8.0%
a1422
 
6.5%
s1204
 
5.5%
d1003
 
4.6%
C892
 
4.0%
o892
 
4.0%
T867
 
3.9%
h867
 
3.9%
Other values (23)7310
33.2%

Ram
Categorical

High correlation 

Distinct7
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size7.1 KiB
16GB
456 
8GB
369 
32GB
 
40
4GB
 
22
64GB
 
3
Other values (2)
 
3

Length

Max length4
Median length4
Mean length3.5610302
Min length3

Characters and Unicode

Total characters3180
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row8GB
2nd row8GB
3rd row8GB
4th row16GB
5th row8GB

Common Values

ValueCountFrequency (%)
16GB456
51.1%
8GB369
41.3%
32GB40
 
4.5%
4GB22
 
2.5%
64GB3
 
0.3%
12GB2
 
0.2%
2GB1
 
0.1%

Length

2026-01-17T03:57:13.214337image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-17T03:57:13.312014image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
ValueCountFrequency (%)
16gb456
51.1%
8gb369
41.3%
32gb40
 
4.5%
4gb22
 
2.5%
64gb3
 
0.3%
12gb2
 
0.2%
2gb1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
G893
28.1%
B893
28.1%
6459
14.4%
1458
14.4%
8369
11.6%
243
 
1.4%
340
 
1.3%
425
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)3180
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
G893
28.1%
B893
28.1%
6459
14.4%
1458
14.4%
8369
11.6%
243
 
1.4%
340
 
1.3%
425
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)3180
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
G893
28.1%
B893
28.1%
6459
14.4%
1458
14.4%
8369
11.6%
243
 
1.4%
340
 
1.3%
425
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)3180
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
G893
28.1%
B893
28.1%
6459
14.4%
1458
14.4%
8369
11.6%
243
 
1.4%
340
 
1.3%
425
 
0.8%

Ram_type
Categorical

Distinct12
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size7.1 KiB
DDR4
499 
DDR5
166 
LPDDR5
145 
LPDDR4X
 
41
LPDDR4
 
14
Other values (7)
 
28

Length

Max length7
Median length4
Mean length4.5711086
Min length3

Characters and Unicode

Total characters4082
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)0.3%

Sample

1st rowDDR4
2nd rowDDR4
3rd rowDDR4
4th rowLPDDR5
5th rowDDR4

Common Values

ValueCountFrequency (%)
DDR4499
55.9%
DDR5166
 
18.6%
LPDDR5145
 
16.2%
LPDDR4X41
 
4.6%
LPDDR414
 
1.6%
LPDDR4x13
 
1.5%
Unified7
 
0.8%
DDR33
 
0.3%
LPDDR5X2
 
0.2%
DDR4-1
 
0.1%
Other values (2)2
 
0.2%

Length

2026-01-17T03:57:13.437906image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ddr4500
56.0%
ddr5166
 
18.6%
lpddr5145
 
16.2%
lpddr4x54
 
6.0%
lpddr414
 
1.6%
unified7
 
0.8%
ddr33
 
0.3%
lpddr5x3
 
0.3%
ddr1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
D1772
43.4%
R886
21.7%
4568
 
13.9%
5314
 
7.7%
L216
 
5.3%
P216
 
5.3%
X43
 
1.1%
x14
 
0.3%
i14
 
0.3%
U7
 
0.2%
Other values (6)32
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)4082
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
D1772
43.4%
R886
21.7%
4568
 
13.9%
5314
 
7.7%
L216
 
5.3%
P216
 
5.3%
X43
 
1.1%
x14
 
0.3%
i14
 
0.3%
U7
 
0.2%
Other values (6)32
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)4082
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
D1772
43.4%
R886
21.7%
4568
 
13.9%
5314
 
7.7%
L216
 
5.3%
P216
 
5.3%
X43
 
1.1%
x14
 
0.3%
i14
 
0.3%
U7
 
0.2%
Other values (6)32
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)4082
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
D1772
43.4%
R886
21.7%
4568
 
13.9%
5314
 
7.7%
L216
 
5.3%
P216
 
5.3%
X43
 
1.1%
x14
 
0.3%
i14
 
0.3%
U7
 
0.2%
Other values (6)32
 
0.8%

ROM
Categorical

Imbalance 

Distinct7
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size7.1 KiB
512GB
634 
1TB
188 
256GB
 
42
128GB
 
12
2TB
 
10
Other values (2)
 
7

Length

Max length5
Median length5
Mean length4.5487122
Min length3

Characters and Unicode

Total characters4062
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row512GB
2nd row512GB
3rd row512GB
4th row512GB
5th row256GB

Common Values

ValueCountFrequency (%)
512GB634
71.0%
1TB188
 
21.1%
256GB42
 
4.7%
128GB12
 
1.3%
2TB10
 
1.1%
64GB5
 
0.6%
32GB2
 
0.2%

Length

2026-01-17T03:57:13.569337image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-17T03:57:13.659888image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
ValueCountFrequency (%)
512gb634
71.0%
1tb188
 
21.1%
256gb42
 
4.7%
128gb12
 
1.3%
2tb10
 
1.1%
64gb5
 
0.6%
32gb2
 
0.2%

Most occurring characters

ValueCountFrequency (%)
B893
22.0%
1834
20.5%
2700
17.2%
G695
17.1%
5676
16.6%
T198
 
4.9%
647
 
1.2%
812
 
0.3%
45
 
0.1%
32
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)4062
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
B893
22.0%
1834
20.5%
2700
17.2%
G695
17.1%
5676
16.6%
T198
 
4.9%
647
 
1.2%
812
 
0.3%
45
 
0.1%
32
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)4062
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
B893
22.0%
1834
20.5%
2700
17.2%
G695
17.1%
5676
16.6%
T198
 
4.9%
647
 
1.2%
812
 
0.3%
45
 
0.1%
32
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)4062
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
B893
22.0%
1834
20.5%
2700
17.2%
G695
17.1%
5676
16.6%
T198
 
4.9%
647
 
1.2%
812
 
0.3%
45
 
0.1%
32
 
< 0.1%

ROM_type
Categorical

Imbalance 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.1 KiB
SSD
872 
Hard-Disk
 
21

Length

Max length9
Median length3
Mean length3.1410974
Min length3

Characters and Unicode

Total characters2805
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSSD
2nd rowSSD
3rd rowSSD
4th rowSSD
5th rowSSD

Common Values

ValueCountFrequency (%)
SSD872
97.6%
Hard-Disk21
 
2.4%

Length

2026-01-17T03:57:13.761427image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-17T03:57:13.820703image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
ValueCountFrequency (%)
ssd872
97.6%
hard-disk21
 
2.4%

Most occurring characters

ValueCountFrequency (%)
S1744
62.2%
D893
31.8%
H21
 
0.7%
a21
 
0.7%
r21
 
0.7%
d21
 
0.7%
-21
 
0.7%
i21
 
0.7%
s21
 
0.7%
k21
 
0.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)2805
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S1744
62.2%
D893
31.8%
H21
 
0.7%
a21
 
0.7%
r21
 
0.7%
d21
 
0.7%
-21
 
0.7%
i21
 
0.7%
s21
 
0.7%
k21
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)2805
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S1744
62.2%
D893
31.8%
H21
 
0.7%
a21
 
0.7%
r21
 
0.7%
d21
 
0.7%
-21
 
0.7%
i21
 
0.7%
s21
 
0.7%
k21
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)2805
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S1744
62.2%
D893
31.8%
H21
 
0.7%
a21
 
0.7%
r21
 
0.7%
d21
 
0.7%
-21
 
0.7%
i21
 
0.7%
s21
 
0.7%
k21
 
0.7%

GPU
Text

Distinct134
Distinct (%)15.0%
Missing0
Missing (%)0.0%
Memory size7.1 KiB
2026-01-17T03:57:13.930000image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/

Length

Max length47
Median length35
Mean length22.62374
Min length8

Characters and Unicode

Total characters20203
Distinct characters51
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique60 ?
Unique (%)6.7%

Sample

1st row4GB AMD Radeon RX 6500M
2nd rowIntel UHD Graphics
3rd rowIntel Iris Xe Graphics
4th rowIntel Integrated Iris Xe
5th rowApple M1 Integrated Graphics
ValueCountFrequency (%)
intel410
11.5%
graphics324
 
9.1%
nvidia305
 
8.6%
geforce297
 
8.4%
rtx267
 
7.5%
amd229
 
6.4%
iris207
 
5.8%
xe199
 
5.6%
integrated196
 
5.5%
radeon176
 
5.0%
Other values (80)941
26.5%
2026-01-17T03:57:14.195759image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2684
 
13.3%
e1787
 
8.8%
I1407
 
7.0%
r1039
 
5.1%
G983
 
4.9%
t803
 
4.0%
n783
 
3.9%
a722
 
3.6%
D697
 
3.4%
c630
 
3.1%
Other values (41)8668
42.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)20203
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2684
 
13.3%
e1787
 
8.8%
I1407
 
7.0%
r1039
 
5.1%
G983
 
4.9%
t803
 
4.0%
n783
 
3.9%
a722
 
3.6%
D697
 
3.4%
c630
 
3.1%
Other values (41)8668
42.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)20203
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2684
 
13.3%
e1787
 
8.8%
I1407
 
7.0%
r1039
 
5.1%
G983
 
4.9%
t803
 
4.0%
n783
 
3.9%
a722
 
3.6%
D697
 
3.4%
c630
 
3.1%
Other values (41)8668
42.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)20203
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2684
 
13.3%
e1787
 
8.8%
I1407
 
7.0%
r1039
 
5.1%
G983
 
4.9%
t803
 
4.0%
n783
 
3.9%
a722
 
3.6%
D697
 
3.4%
c630
 
3.1%
Other values (41)8668
42.9%

display_size
Real number (ℝ)

Distinct18
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.173751
Minimum11.6
Maximum18
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2026-01-17T03:57:14.282903image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/

Quantile statistics

Minimum11.6
5-th percentile14
Q114
median15.6
Q315.6
95-th percentile16
Maximum18
Range6.4
Interquartile range (IQR)1.6

Descriptive statistics

Standard deviation0.93909503
Coefficient of variation (CV)0.061889444
Kurtosis0.56612155
Mean15.173751
Median Absolute Deviation (MAD)0
Skewness-0.80858315
Sum13550.16
Variance0.88189948
MonotonicityNot monotonic
2026-01-17T03:57:14.391371image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
15.6464
52.0%
14214
24.0%
16112
 
12.5%
13.328
 
3.1%
16.121
 
2.4%
17.314
 
1.6%
14.17
 
0.8%
11.67
 
0.8%
175
 
0.6%
13.44
 
0.4%
Other values (8)17
 
1.9%
ValueCountFrequency (%)
11.67
 
0.8%
13.328
 
3.1%
13.44
 
0.4%
13.52
 
0.2%
13.62
 
0.2%
14214
24.0%
14.17
 
0.8%
14.24
 
0.4%
153
 
0.3%
15.32
 
0.2%
ValueCountFrequency (%)
181
 
0.1%
17.314
 
1.6%
175
 
0.6%
16.22
 
0.2%
16.121
 
2.4%
16112
 
12.5%
15.6464
52.0%
15.561
 
0.1%
15.32
 
0.2%
153
 
0.3%

resolution_width
Real number (ℝ)

High correlation 

Distinct18
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2035.3931
Minimum1080
Maximum3840
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2026-01-17T03:57:14.506270image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/

Quantile statistics

Minimum1080
5-th percentile1366
Q11920
median1920
Q31920
95-th percentile2880
Maximum3840
Range2760
Interquartile range (IQR)0

Descriptive statistics

Standard deviation426.07601
Coefficient of variation (CV)0.20933353
Kurtosis4.9916138
Mean2035.3931
Median Absolute Deviation (MAD)0
Skewness1.8360547
Sum1817606
Variance181540.77
MonotonicityNot monotonic
2026-01-17T03:57:14.656686image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
1920680
76.1%
256070
 
7.8%
136641
 
4.6%
288040
 
4.5%
384014
 
1.6%
320011
 
1.2%
10808
 
0.9%
16005
 
0.6%
30244
 
0.4%
34564
 
0.4%
Other values (8)16
 
1.8%
ValueCountFrequency (%)
10808
 
0.9%
12004
 
0.4%
12802
 
0.2%
136641
 
4.6%
14401
 
0.1%
16005
 
0.6%
1920680
76.1%
21603
 
0.3%
22402
 
0.2%
22561
 
0.1%
ValueCountFrequency (%)
384014
 
1.6%
34564
 
0.4%
320011
 
1.2%
30721
 
0.1%
30244
 
0.4%
288040
4.5%
256070
7.8%
24962
 
0.2%
22561
 
0.1%
22402
 
0.2%

resolution_height
Real number (ℝ)

High correlation 

Distinct22
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1218.3247
Minimum768
Maximum3456
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2026-01-17T03:57:14.790188image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/

Quantile statistics

Minimum768
5-th percentile1080
Q11080
median1080
Q31200
95-th percentile1920
Maximum3456
Range2688
Interquartile range (IQR)120

Descriptive statistics

Standard deviation326.75688
Coefficient of variation (CV)0.26820179
Kurtosis5.7277028
Mean1218.3247
Median Absolute Deviation (MAD)0
Skewness2.1707962
Sum1087964
Variance106770.06
MonotonicityNot monotonic
2026-01-17T03:57:14.929364image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
1080577
64.6%
1200101
 
11.3%
160053
 
5.9%
76841
 
4.6%
180034
 
3.8%
144015
 
1.7%
192013
 
1.5%
240011
 
1.2%
20009
 
1.0%
25606
 
0.7%
Other values (12)33
 
3.7%
ValueCountFrequency (%)
76841
 
4.6%
10242
 
0.2%
1080577
64.6%
1200101
 
11.3%
12801
 
0.1%
14002
 
0.2%
144015
 
1.7%
15041
 
0.1%
160053
 
5.9%
16206
 
0.7%
ValueCountFrequency (%)
34561
 
0.1%
25606
 
0.7%
240011
 
1.2%
22342
 
0.2%
21606
 
0.7%
20009
 
1.0%
19644
 
0.4%
192013
 
1.5%
18642
 
0.2%
180034
3.8%

OS
Categorical

Imbalance 

Distinct14
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size7.1 KiB
Windows 11 OS
782 
Windows 10 OS
 
28
DOS OS
 
18
Windows 11 OS
 
15
Mac OS
 
12
Other values (9)
 
38

Length

Max length18
Median length13
Mean length12.714446
Min length6

Characters and Unicode

Total characters11354
Distinct characters33
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)0.3%

Sample

1st rowWindows 11 OS
2nd rowWindows 11 OS
3rd rowWindows 11 OS
4th rowWindows 11 OS
5th rowMac OS

Common Values

ValueCountFrequency (%)
Windows 11 OS782
87.6%
Windows 10 OS28
 
3.1%
DOS OS18
 
2.0%
Windows 11 OS15
 
1.7%
Mac OS12
 
1.3%
Windows 10 OS10
 
1.1%
Chrome OS10
 
1.1%
Windows OS9
 
1.0%
Ubuntu OS2
 
0.2%
DOS 3.0 OS2
 
0.2%
Other values (4)5
 
0.6%

Length

2026-01-17T03:57:15.095438image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
os893
34.0%
windows844
32.1%
11798
30.4%
1038
 
1.4%
dos20
 
0.8%
mac16
 
0.6%
chrome10
 
0.4%
ubuntu2
 
0.1%
3.02
 
0.1%
10.15.32
 
0.1%
Other values (4)4
 
0.2%

Most occurring characters

ValueCountFrequency (%)
1761
15.5%
11638
14.4%
S914
8.1%
O913
8.0%
o855
7.5%
n848
7.5%
i848
7.5%
d846
7.5%
W844
7.4%
s844
7.4%
Other values (23)1043
9.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)11354
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1761
15.5%
11638
14.4%
S914
8.1%
O913
8.0%
o855
7.5%
n848
7.5%
i848
7.5%
d846
7.5%
W844
7.4%
s844
7.4%
Other values (23)1043
9.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)11354
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1761
15.5%
11638
14.4%
S914
8.1%
O913
8.0%
o855
7.5%
n848
7.5%
i848
7.5%
d846
7.5%
W844
7.4%
s844
7.4%
Other values (23)1043
9.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)11354
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1761
15.5%
11638
14.4%
S914
8.1%
O913
8.0%
o855
7.5%
n848
7.5%
i848
7.5%
d846
7.5%
W844
7.4%
s844
7.4%
Other values (23)1043
9.2%

warranty
Categorical

Imbalance 

Distinct4
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size7.1 KiB
1
819 
2
 
59
3
 
9
0
 
6

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters893
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1819
91.7%
259
 
6.6%
39
 
1.0%
06
 
0.7%

Length

2026-01-17T03:57:15.244989image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-17T03:57:15.356387image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
ValueCountFrequency (%)
1819
91.7%
259
 
6.6%
39
 
1.0%
06
 
0.7%

Most occurring characters

ValueCountFrequency (%)
1819
91.7%
259
 
6.6%
39
 
1.0%
06
 
0.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)893
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1819
91.7%
259
 
6.6%
39
 
1.0%
06
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)893
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1819
91.7%
259
 
6.6%
39
 
1.0%
06
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)893
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1819
91.7%
259
 
6.6%
39
 
1.0%
06
 
0.7%

Interactions

2026-01-17T03:57:08.555285image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2026-01-17T03:57:04.062444image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2026-01-17T03:57:04.840721image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2026-01-17T03:57:05.567150image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2026-01-17T03:57:06.287528image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2026-01-17T03:57:07.037477image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2026-01-17T03:57:07.790557image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2026-01-17T03:57:08.671044image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2026-01-17T03:57:04.194683image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2026-01-17T03:57:04.937680image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2026-01-17T03:57:05.671468image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2026-01-17T03:57:06.385522image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2026-01-17T03:57:07.146008image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2026-01-17T03:57:07.895898image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2026-01-17T03:57:08.790045image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2026-01-17T03:57:04.292193image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2026-01-17T03:57:05.039816image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2026-01-17T03:57:05.767365image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2026-01-17T03:57:06.491328image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2026-01-17T03:57:07.252592image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2026-01-17T03:57:08.000154image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2026-01-17T03:57:08.935635image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2026-01-17T03:57:04.392756image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2026-01-17T03:57:05.139637image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2026-01-17T03:57:05.860690image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2026-01-17T03:57:06.596273image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2026-01-17T03:57:07.353629image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2026-01-17T03:57:08.119304image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2026-01-17T03:57:09.050662image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2026-01-17T03:57:04.495486image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2026-01-17T03:57:05.246312image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2026-01-17T03:57:05.962447image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2026-01-17T03:57:06.706828image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2026-01-17T03:57:07.477316image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2026-01-17T03:57:08.227582image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2026-01-17T03:57:09.176365image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2026-01-17T03:57:04.625937image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2026-01-17T03:57:05.362388image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2026-01-17T03:57:06.079440image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2026-01-17T03:57:06.812296image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2026-01-17T03:57:07.578124image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2026-01-17T03:57:08.333721image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2026-01-17T03:57:09.331676image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2026-01-17T03:57:04.738259image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2026-01-17T03:57:05.466486image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2026-01-17T03:57:06.181237image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2026-01-17T03:57:06.923224image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2026-01-17T03:57:07.687520image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2026-01-17T03:57:08.442376image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/

Correlations

2026-01-17T03:57:15.462418image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
CPUOSROMROM_typeRamRam_typeUnnamed: 0Unnamed: 0.1branddisplay_sizepriceresolution_heightresolution_widthspec_ratingwarranty
CPU1.0000.4210.4470.2600.5550.4250.1140.1180.2510.4460.4320.3800.4290.2910.088
OS0.4211.0000.3840.3970.2460.3310.1240.1470.4310.2890.1230.2870.2510.0320.352
ROM0.4470.3841.0000.4400.4900.2800.0690.0620.4290.2970.3170.2170.2460.2340.040
ROM_type0.2600.3970.4401.0000.3730.1810.1250.0890.2780.3190.0890.1580.1120.0000.000
Ram0.5550.2460.4900.3731.0000.3170.0600.0580.4810.3200.4870.2390.2940.2820.061
Ram_type0.4250.3310.2800.1810.3171.0000.0970.0860.4060.3320.2140.2520.2570.1860.092
Unnamed: 00.1140.1240.0690.1250.0600.0971.0001.0000.1650.0050.1850.0820.0100.0760.105
Unnamed: 0.10.1180.1470.0620.0890.0580.0861.0001.0000.1590.0050.1850.0820.0100.0760.109
brand0.2510.4310.4290.2780.4810.4060.1650.1591.0000.3710.1120.3230.3160.0860.455
display_size0.4460.2890.2970.3190.3200.3320.0050.0050.3711.0000.2680.1130.1750.3470.082
price0.4320.1230.3170.0890.4870.2140.1850.1850.1120.2681.0000.6130.5150.4320.102
resolution_height0.3800.2870.2170.1580.2390.2520.0820.0820.3230.1130.6131.0000.7020.2760.082
resolution_width0.4290.2510.2460.1120.2940.2570.0100.0100.3160.1750.5150.7021.0000.2530.070
spec_rating0.2910.0320.2340.0000.2820.1860.0760.0760.0860.3470.4320.2760.2531.0000.089
warranty0.0880.3520.0400.0000.0610.0920.1050.1090.4550.0820.1020.0820.0700.0891.000

Missing values

2026-01-17T03:57:09.601110image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
A simple visualization of nullity by column.
2026-01-17T03:57:09.788340image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Unnamed: 0.1Unnamed: 0brandnamepricespec_ratingprocessorCPURamRam_typeROMROM_typeGPUdisplay_sizeresolution_widthresolution_heightOSwarranty
000HPVictus 15-fb0157AX Gaming Laptop4990073.0000005th Gen AMD Ryzen 5 5600HHexa Core, 12 Threads8GBDDR4512GBSSD4GB AMD Radeon RX 6500M15.61920.01080.0Windows 11 OS1
111HP15s-fq5007TU Laptop3990060.00000012th Gen Intel Core i3 1215UHexa Core (2P + 4E), 8 Threads8GBDDR4512GBSSDIntel UHD Graphics15.61920.01080.0Windows 11 OS1
222AcerOne 14 Z8-415 Laptop2699069.32352911th Gen Intel Core i3 1115G4Dual Core, 4 Threads8GBDDR4512GBSSDIntel Iris Xe Graphics14.01920.01080.0Windows 11 OS1
333LenovoYoga Slim 6 14IAP8 82WU0095IN Laptop5972966.00000012th Gen Intel Core i5 1240P12 Cores (4P + 8E), 16 Threads16GBLPDDR5512GBSSDIntel Integrated Iris Xe14.02240.01400.0Windows 11 OS1
444AppleMacBook Air 2020 MGND3HN Laptop6999069.323529Apple M1Octa Core (4P + 4E)8GBDDR4256GBSSDApple M1 Integrated Graphics13.32560.01600.0Mac OS1
555AcerExtensa EX214-53 Laptop3999062.00000012th Gen Intel Core i5 1240P12 Cores (4P + 8E), 16 Threads8GBDDR4512GBSSDIntel Iris Xe Graphics14.01920.01080.0Windows 11 OS1
666DellInspiron 3520 D560896WIN9B Laptop3679060.00000012th Gen Intel Core i3 1215UHexa Core (2P + 4E), 8 Threads8GBDDR4512GBSSDIntel UHD Graphics15.61920.01080.0Windows 11 OS1
777AcerNitro V ANV15-51 2023 Gaming Laptop7699063.00000013th Gen Intel Core i5 13420HOcta Core (4P + 4E), 12 Threads16GBDDR5512GBSSD6GB NVIDIA GeForce RTX 405015.61920.01080.0Windows 11 OS1
888AsusVivobook 15 X1502ZA-EJ523WS Laptop4899064.00000012th Gen Intel Core i5 12500H12 Cores (4P + 8E), 16 Threads8GBDDR4512GBSSDIntel Iris Xe15.61920.01080.0Windows 11 OS1
999SamsungGalaxy Book2 Pro 13 Laptop7499068.00000012th Gen Intel Core i5 1240P12 Cores (4P + 8E), 16 Threads16GBLPDDR5512GBSSDIntel Iris Xe Graphics13.31080.01920.0Windows 11 OS1
Unnamed: 0.1Unnamed: 0brandnamepricespec_ratingprocessorCPURamRam_typeROMROM_typeGPUdisplay_sizeresolution_widthresolution_heightOSwarranty
8839211010DellG15-5530 Gaming Laptop11999073.00000013th Gen Intel Core i7 13650HX14 Cores (6P + 8E)16GBDDR5512GBSSD6GB NVIDIA GeForce RTX 405015.61920.01080.0Windows 11 OS1
8849221011Dell‎G16-7630 Gaming Laptop18749079.00000013th Gen Intel Core i9 13900HX24 Cores (8P + 16E), 32 Threads16GBDDR51TBSSD8GB NVIDIA GeForce RTX 406016.02560.01600.0Windows 11 OS1
8859231012DellG15-5530 2023 Gaming Laptop12569975.00000013th Gen Intel Core i7 13650HX14 Cores (6P + 8E)16GBDDR5512GBSSD6GB NVIDIA GeForce RTX 405015.61920.01080.0Windows 11 OS1
8869241013AcerAspire Vero AV14-52P NX.KJTSI.002 Laptop4999069.32352913th Gen Intel Core i3 1315UHexa Core (2P + 4E), 8 Threads16GBLPDDR4X512GBSSDIntel Integrated UHD14.01920.01080.0Windows OS1
8879251014AcerAspire 5 A515-58M NX.KHGSI.002 Gaming Laptop5699069.32352913th Gen Intel Core i5 1335U10 Cores (2P + 8E), 12 Threads16GBLPDDR5512GBSSDIntel Integrated Iris Xe15.61920.01080.0Windows 11 OS1
8889261015AsusVivobook 15X 2023 K3504VAB-NJ321WS Laptop4499069.32352913th Gen ‎Intel Core i3 1315UHexa Core (2P + 4E), 8 Threads8GBDDR4512GBSSDIntegrated Intel UHD Graphics15.61920.01080.0Windows 11 OS1
8899271016AsusTUF A15 FA577RM-HQ032WS Laptop11000071.0000006th Gen AMD Ryzen 7 6800HOcta Core, 16 Threads16GBDDR1TBSSD6GB NVIDIA GeForce RTX 306015.62560.01440.0Windows 11 OS1
8909281017AsusROG Zephyrus G14 2023 GA402XV-N2034WS Gaming Laptop18999089.0000007th Gen AMD Ryzen 9 7940HSOcta Core, 16 Threads32GBDDR51TBSSD8GB NVIDIA GeForce RTX 406014.02560.01600.0Windows 11 OS1
8919291018AsusTUF Gaming F15 2023 FX507VU-LP083WS Gaming Laptop12999073.00000013th Gen Intel Core i7 13700H14 Cores (6P + 8E), 20 Threads16GBDDR4512GBSSD6GB NVIDIA GeForce RTX 405015.61920.01080.0Windows 11 OS1
8929301019AsusTUF Gaming A15 2023 FA577XU-LP041WS Gaming Laptop13199084.0000007th Gen AMD Ryzen 9 7940HSOcta Core, 16 Threads16GBDDR41TBSSD6GB NVIDIA GeForce RTX 405015.61920.01080.0Windows 11 OS1